Forests, Volume 14, Issue 12 (December 2023) – 189 articles

The objective of this study is to examine the impact of climate change on forestry efficiency (FRE) and total factor productivity change (TFPC) in 31 provinces of China for a study period of 2001–2020. Additionally, the study aims to evaluate the success level of governmental initiatives used to mitigate climate change. Using the DEA-SBM, this study estimates the forestry efficiency for 31 Chinese provinces and seven regions. Results indicate that the average forestry efficiency score obtained is 0.7155. After considering climatic factors, the efficiency level is 0.5412. East China demonstrates the highest average efficiency with a value of 0.9247, while the lowest score of 0.2473 is observed in Northwest China. Heilongjiang, Anhui, Yunnan, and Tibet exhibit the highest efficiency scores. Mongolia, Heilongjiang, Sichuan, Hebei, and Hunan are the five provinces most affected by climate change. This study’s findings indicate that the average total factor forestry productivity (TFPC) is 1.0480, representing an increase of 4.80%. The primary determinant for change is technology change (TC), which surpasses efficiency change (EC). Including climate variables reduces total factor productivity change (TFPC) to 1.0205, mainly driven by a decrease in TC. The region of South China exhibits the highest total factor productivity change (TFPC) with a value of 1.087, whereas both Northeast China and Central China observe falls below 1 in TFPC. The Mann–Whitney U test provides evidence of statistically significant disparities in forestry efficiency and TFPC scores when estimated with and without incorporating climate factors. Kruskal–Wallis found a statistically significant difference in FRE and TFPC among seven regions. Full article

The recuperation factors (negative air ion concentration, airborne particulate matter, human comfort index, and acoustic environment index) of coastal green spaces have significant health effects. Most current studies focus on the distribution pattern of single recuperation factors in the forest environment; however, the comprehensive health effects of coastal green spaces are still unknown. To address this, we analyzed the distribution patterns of single and comprehensive health factors in different landscape configurations, landscape compositions, and coastal distances by principal component analysis and systematic clustering. The results show that: (1) coniferous and broadleaf mixed forests exhibit higher integrated health benefits than other landscape compositions; (2) closed and partially closed landscape configurations exhibit enhanced potential for promoting health benefits as opposed to partially open and open spaces; (3) a coastal distance of 150–300 m offers the strongest comprehensive health benefits. These findings collectively suggest that the increased cultivation of closed and partially closed mixed coniferous and broadleaf forest species at a distance of 150–300 m could effectively provide higher comprehensive health effects. Our study complements the ecosystem service of coastal green areas, especially in coastal health ecological services, providing support for coastal rehabilitation landscape planning; and can help to guide tourists in scheduling coastal health activities scientifically. Full article

Sea buckthorn (Hippophae rhamnoides L.), as an economically and ecologically valuable plant with rich nutritional and bioactive compounds, has garnered significant interest. The demand for Sea buckthorn has explosive growth, highlighting the urgent need for the cultivation of fast-growing, high-quality Sea buckthorn seedlings. However, there are still some controversies in Sea buckthorn germplasm resource research. This review provides a comprehensive summary of the recent research findings on Sea buckthorn plants, encompassing their classification, distribution, propagation methods, medical functions, and valorization. It aims to offer strong support for the industrial utilization of the Sea buckthorn and explores the prospects for molecular breeding in Sea buckthorn. Full article

The application of nitrogen fertilizer is crucial in the cultivation of bamboo forests, and comprehending the alterations in soil organic carbon (SOC) due to nitrogen application is essential for monitoring soil quality. Predicting the dynamics of soil carbon stock involves analyzing two components: particulate organic carbon (POC) and mineral-associated organic carbon (MAOC). This study aimed to investigate the impact of high nitrogen inputs on SOC stock in Moso bamboo forests located in southwestern China. The research focused on analyzing changes in soil chemical properties, SOC content, and its components (POC and MAOC), as well as microbial biomass in the surface layer (0–10 cm) under different nitrogen applications (0, 242, 484, and 726 kg N ha⁻¹ yr⁻¹). The results indicate that nitrogen application significantly reduced the SOC content, while concurrently causing a significant increase in POC content and a decrease in MAOC content within the Moso bamboo forest (p < 0.05). The HM treatment notably increased the NO₃⁻-N content to 2.15 mg/kg and decreased the NH₄⁺-N content to 11.29 mg/kg, although it did not significantly influence the microbial biomass carbon (MBC) and nitrogen (MBN). The LN and MN treatments significantly reduced the MBC and MBN contents (71.6% and 70.8%, 62.5% and 56.8%). Nitrogen application significantly increased the Na⁺ concentration, with a peak observed under the LN treatment (135.94 mg/kg, p < 0.05). The MN treatment significantly increased the concentrations of Fe³⁺ and Al³⁺ (p < 0.05), whereas nitrogen application did not significantly affect Ca²⁺, Mg²⁺ concentration, and cation exchange capacity (p > 0.05). Correlation and redundancy analyses (RDAs) revealed that the increase in annual litterfall did not significantly correlate with the rise in POC, and changes in extractable cations were not significantly correlated with the decrease in MAOC. Soil nitrogen availability, MBC, and MBN were identified as the primary factors affecting POC and MAOC content. In conclusion, the application of nitrogen has a detrimental impact on the soil organic carbon (SOC) of Moso bamboo forests. Consequently, it is imperative to regulate fertilization levels in order to preserve soil quality when managing these forests. Our research offers a theoretical foundation for comprehending and forecasting alterations in soil carbon stocks within bamboo forest ecosystems, thereby bolstering the sustainable management of Moso bamboo forests. Full article

During the growth of a forest insect outbreak epicenter, there are processes that involve the formation and expansion of the primary epicenter of forest damage, as well as secondary epicenters—both connected and unconnected to the primary one. This study characterizes outbreak epicenters in terms of their fractal dimensions and “viscous finger” parameters at the epicenter boundary, highlighting their significance in the context of forest insect management. Local outbreak epicenters were found to be characterized by fractal dimension D = 1.4–1.5, and the boundaries of the epicenters were described using the “viscous finger” model. Proposed models were constructed and validated using remote sensing data obtained from MODIS and Sentinel-2 satellites at epicenter sites and boundaries during the outbreak of the Siberian silk moth Dendrolimus sibiricus Tschetverikov from 2014 to 2020 in the Krasnoyarsk region of Russia. The study revealed that the frequency of the mode spectrum of one-stage spatial series of “viscous fingers” corresponds with the data on the development of the outbreak foci area. Full article

Evaluating the long-term urban forest ecological environmental quality (EEQ) and analyzing the drivers of its spatiotemporal changes can provide a scientific basis for making long-term urban forest planning decisions. Taking into account the characteristics of urban forest parks with low area proportions of construction land and bare land, high vegetation coverage, and serious forest disturbances, we constructed a modified urban forest park EEQ evaluation index based on a remote sensing ecological index named MRSEI, which is composed of the Landsat enhanced vegetation index (EVI), wetness, land surface temperature (LST), and forest disturbance index (FDI). We selected the Nanjing Zijin Mountain National Forest Park as the study area, used landsat time series stack (LTSS) remote sensing images from 1990 to 2020 as the main data source, and adopted the suggested modified MRSEI, the Theil-Sen median method, and the Hurst index to evaluate the EEQ to analyze its spatiotemporal variations and its driving factors in the study area. The main research results were as follows: (1) the EEQ of Zijin Mountain showed an up-and-down, overall slowly increasing trend from 1990 to 2020, while the spatial auto-correlation coefficient showed an overall decreasing trend; (2) the area percentage of the EEQ-persistent region accounted for 78.69%, and the anti-sustainable region accounted for 21.31%; (3) the spatial centers of the EEQ in the study area were mainly concentrated on the middle and upper part of the southern slope of Zijin Mountain, moving southward from 1990 to 2020; (4) the analysis of drivers showed that climate factors, forest landscape structure, forest disturbances, and forest growth conditions were the main driving factors affecting the EEQ in the study area. These results provide a research framework for the analysis of EEQ changes over a long-term period in the urban forest parks of China. Full article

Global change in the climate is affecting tree/forest growth. There have been many studies that analyzed climate effects on tree growth. Results presented in these studies showed that the climate had both positive and negative effects on tree growth. The nature (positive/negative) and magnitude of the effects and the climate variables affecting growth depended on tree species. Climate-sensitive diameter growth models are not available for white pine (Pinus strobus L.) and white spruce (Picea glauca (Moench) Voss) plantations. These models are needed to project forest growth and yield and develop forest management plans. Therefore, diameter growth models were developed for white pine and white spruce plantations by incorporating climate variables. Four hundred white pine and white spruce trees (200 per species) were sampled from 80 (40 per species) even-aged monospecific plantations (five trees per plantation) across Ontario, Canada. Diameter–age pairs were obtained from these trees using stem analysis. A nonlinear mixed-effects modeling approach was used to develop diameter growth models. To make the models climate sensitive, model parameters were expressed in term of climate variables. Inclusion of climate variables significantly improved model fit statistics and predictive accuracy. For evaluation, diameters (inside bark) at breast height were estimated for three geographic locations (east, west, and south) across Ontario for an 80-year growth period (2021–2100) under three climate change (emissions) scenarios (representative concentration pathway or RCP 2.6, 4.5, and 8.5 watts m⁻²). For both species, the overall climate effects were negative. For white spruce, the maximum pronounced difference in projected diameters after the 80-year growth period was in the west. At this location, compared to the no climate change scenario, projected spruce diameters under RCPs 2.6 and 8.5 were thinner by 4.64 (15.99%) and 3.72 (12.80%) cm, respectively. For white pine, the maximum difference was in the south. Compared to the no climate change scenario, projected pine diameters at age 80 under RCPs 2.6 and 8.5 at this location were narrower by 4.54 (13.99%) and 7.60 (23.43%) cm, respectively. For both species, climate effects on diameter growth were less evident at other locations. If the values of climate variables are unavailable, models fitted without climate variables can be used to estimate these diameters for both species. Full article

Aiming to enhance the efficiency and precision of multi-objective optimization in southwestern secondary growth of Pinus yunnanensis forests, this study integrated spatial and non-spatial structural indicators to establish objective functions and constraints for assessing forest structure. Felling decisions were made using the random selection method (RSM), Q-value method (QVM), and V-map method (VMM). Actions taken to optimize the forest stand structure (FSS) through tree selection were approached as decisions by a reinforcement learning (RL) agent. Leveraging RL’s trial-and-error strategy, we continually refined the agent’s decision-making process, applying it to multi-objective optimization. Simulated felling experiments conducted across circular sample plots (P1–P4) compared RL, Monte Carlo (MC), and particle swarm optimization (PSO) in FSS optimization. Notable enhancements in the values of the objective function (VOFs) were observed across all plots. RL-based strategies exhibited improvements, achieving VOF increases of 17.24%, 44.92%, 34.66%, and 17.10% for P1–P4, respectively, outperforming MC-based (10.73%, 41.54%, 30.39%, and 15.07%, respectively) and PSO-based (14.08%, 37.78%, 26.17%, and 16.23%, respectively) approaches. The hybrid M7 scheme, integrating RL with the RSM, consistently outperformed other schemes across all plots, yielding an average 26.81% increase in VOF compared to the average enhancement of all schemes (17.42%). This study significantly advances the efficacy and precision of multi-objective optimization strategies for Pinus yunnanensis secondary forests, emphasizing RL’s superior optimization performance, particularly when combined with the RSM, highlighting its potential for optimizing sustainable forest management strategies. Full article

In this work, a series of laboratory surface fire experiments were performed over a pine needle fuel bed to investigate the effectiveness of a firebreak and the behaviors of a surface fire across a firebreak. Seven wind velocities of 0~3.0 m/s and six firebreak widths of 10~35 cm are varied. The behaviors of a surface fire across the firebreak, the heat flux received by fuel surface and fuel temperature before and after the firebreak are analyzed and compared simultaneously. The main conclusions are as follows: the behaviors of a surface fire spreading across a firebreak under different wind velocities are classified into three categories—no ignition, ignition by flame contact and ignition by spot fires. When the wind velocity is not more than 1.0 m/s, the surface fire cannot successfully cross the firebreak; as wind velocity changes from 1.5 m/s to 2.5 m/s, the fuel after the firebreak can be ignited by flame contact for relatively narrow firebreak conditions; when the wind velocity increases to 3.0 m/s, the burning fuel can be blown away along the fuel bed, and the fuel behind the firebreak will be ignited by spot fire. A linear relationship between the threshold of firebreak width and the fireline intensity is obtained, and the linear fitting coefficient in this paper is larger than the results reported by Wilson (0.36). For no ignition conditions, the fuel temperature and the heat flux received by the fuel after firebreak are significantly lower than those before the firebreak, whereas their variations over time are similar to those before the firebreak for ignition conditions. Moreover, for no ignition conditions, the maximum fuel temperature and the heat flux after the firebreak increase with wind velocity, but decrease with firebreak width. Additionally, when the fuel temperature (253 °C) and the heat flux received by the fuel considering the radiation and convection (43 kW/m²) after firebreak exceed a threshold value, the surface fire can successfully cross the firebreak. Full article

In order to investigate the mechanism of the effect of asymmetric reinforcement on the static-bending properties of wood, this paper tests and analyzes the static-bending properties of SPF wood and seven different types of asymmetric fiber surface-reinforced wood (AFRWC) formed by SPF wood as the substrate and bamboo and carbon fibers as the reinforcement materials. The results of the study found that (1) the moduli of rupture of the seven types of AFRWC were increased to varying degrees, but the static-bending moduli of elasticity increased or decreased; (2) the asymmetric reinforcement changed the cross-section strain distribution and damage type of the wood in static bending; (3) the results of the cross-section strain-field tests and the ABAQUS finite element simulation showed that the asymmetric reinforcement method of bonding the bamboo material and the two layers of CFRP in the compression and tensile zones, respectively, can greatly enhance the static-bending performance of the wood. The error between the simulated and measured values of specimens MOR and MOE is only −0.7% and −7.3%, respectively. This type of asymmetric reinforcement makes it possible to obtain a more reasonable cross-section stress distribution. Full article

Timely and reliable estimation of forest stock volume is essential for sustainable forest management and conservation. Light detection and ranging (LiDAR) data can provide an effective depiction of the three-dimensional structure information of forests, but its large-scale application is hampered by spatial continuity. This study aims to construct a LiDAR sampling framework, combined with multi-sensor imagery, to estimate the regional forest stock volume of natural secondary forests in Northeast China. Two sampling approaches were compared, including systematic sampling and classification-based sampling. First, the forest stock volume was mapped using a combination of field measurement data and full-coverage LiDAR data. Then, the forest stock volume obtained in the first step of estimation was used as a reference value, and optical images and topographic features were combined for secondary modeling to compare the effectiveness and accuracy of different sampling methods, including 12 systematic sampling and classification-based sampling methods. Our results show that the root mean square error (RMSE) of the 12 systematic sampling approaches ranged from 55.81 to 57.42 m³/ha, and the BIAS ranged from 21.55 to 24.89 m³/ha. The classification-based LiDAR sampling approach outperformed systematic sampling, with an RMSE of 55.56 (<55.81 m³/ha) and a BIAS of 20.68 (<21.55 m³/ha). This study compares different LiDAR sampling approaches and explores an effective LiDAR sample collection scheme for estimating forest stock, while balancing cost and accuracy. The classification-based LiDAR sampling approach described in this study is easy to apply and portable and can provide a reference for future LiDAR sample collection. Full article

In 2022, the Russian forest sector was severely affected by the government’s ban on the export of unprocessed timber and trade sanctions imposed by several countries. It is generally recognized that the regions of the Russian North-West are the most affected by trade barriers that have emerged. Against this background, the impact of bilateral trade restrictions on timber companies in the Asian part of Russia is not discussed. Nevertheless, the forest industry is an important sector of the Siberian economy that has an economic, social and environmental impact on the life of local communities. This paper analyzes the differences among Siberian timber companies in their response to the crisis depending on three factors: industrial specialization, scale of revenue and regional location. The results show that in 2022 the highest median revenues and net profits were generated by small firms that were focused on the domestic market and benefited from reduced competition due to sanctions. There is also evidence that spatial heterogeneity in the response to the crisis may be due to the different support measures of regional authorities and the proximity of the region to border points. We argue that the current conditions may become a new driver for the timber industry development, aimed at the growth of added value and expansion of domestic demand for wood products. Full article

The rapid-developed scale of China’s trade in wood forest products has undergone a considerable uptick. Concomitant with the evolution of high-quality development paradigms, product quality within trade frameworks has gained escalating scrutiny. Based on the statistical analysis of the export characteristics of China’s wood forest products, this study leverages BACI data spanning from 1998 to 2017. Utilizing regression-based inverse methods, the study quantifies the quality attributes of these export products, dissects fluctuations in quality, and places particular emphasis on the markets within “Belt and Road Initiative” economies to elucidate dynamic trends and spatial distribution characteristics of export quality in this geoeconomic domain. Based on this, the fixed effect model, random effect model, and system GMM are used to empirically examine the influencing factors of China’s wood forest product export quality. This study found that wood-based panel products have the highest quality, followed by paper products and wood furniture among the three major categories of wood forest products. Besides, the overall quality levels of the three products exported to countries participating in the Belt and Road initiative haven’t significantly changed, while notable changes are evident across divergent export destination markets. In addition, an empirical study on the influencing factors of the export product quality of wood forest products is conducted, which indicates that total factor productivity, R&D investment, capital intensity, labor costs, and foreign direct investment are influencing factors. Finally, based on the research conclusions, suggestions are provided on how to improve the export quality of wood forest products. Full article

Poor bucking decisions in forest stands can result in underestimating the profitability of timber sales. This study focuses on Pinus densiflora, commonly known as a red pine in Korea, which has often been underutilized as pulp and chips, leading to reduced profit margins. This study aimed to improve bucking decisions for red pine by analyzing the potential values in different log types and the profitability of manufacturing lumber products compared to pulp chips. A log sawing simulation model was developed using dynamic programming. This study optimized sawing patterns and estimated net profits for varying log sizes within the lumber market in Korea. The findings reveal that manufacturing lumber products from 3.6 m and 2.7 m logs can yield net profits 861% and 723% higher, respectively, than producing pulp chips from 1.8 m logs. Notably, sawing 3.6 m logs resulted in an average net profit 24% higher than from 2.7 m logs. These results advocate for more strategic bucking decisions based on potential timber sale profits and the end-uses of logs, especially in trees with large diameters at breast height (DBH), which can produce high-quality logs and should be bucked into long sawlogs whenever possible. Additionally, the study emphasizes the importance of practicing timber cruise to appraise the stumpage value of forest stands more accurately, moving beyond mere volume estimation to include tree type and expected volume. By implementing these practices, timber sale profits and the overall value of forest stands in Korea can be significantly enhanced. This approach not only benefits the economic aspect of forestry but also encourages sustainable and efficient resource management. Full article

The macroscopic and microscopic anatomical characteristics of wood impact its utilization. This study investigated and compared the anatomical characteristics of six Korean oak wood species: Quercus variabilis, Quercus serrata, Quercus mongolica, Quercus dentata, Quercus aliena, and Quercus acutissima. Microscopic anatomical characteristics were evaluated according to the International Association of Wood Anatomists’ list for hardwood identification. Q. variabilis had a corky bark texture, with a color similar to that of Q. serrata. Flat ridges and shallow-fissured barks were observed in Q. serrata and Q. mongolica. The heartwood color was darker than that of sapwood in all species, with color variations. Q. variabilis had heartwood–sapwood colors similar to those of Q. acutissima, while Q. mongolica and Q. aliena presented similar heartwood–sapwood colors. Concerning microscopic features, Q. variabilis and Q. acutissima exhibited similar latewood vessel arrangements, featuring diagonal and/or radial patterns. In contrast, dendritic-to-diagonal patterns of vessels with angular outlines were observed in Q. serrata, Q. mongolica, Q. dentata, and Q. aliena. Additionally, Q. variabilis and Q. acutissima had vasicentric, confluent, and unilateral paratracheal axial parenchyma in the latewood. In summary, bark morphology, bark color, wood color, and latewood vessel characteristics can be used as identification keys for Korean oak species. Full article

In the initial stages of restoring rocky desertification, the proliferation of nutrients strongly influences plant survival. The carbon–nitrogen doctrine in plants argues that a lower leaf carbon–nitrogen (C:N) ratio enhances the growth of plant nutrients. However, the mechanisms by which inoculation with arbuscular mycorrhizal fungi (AMF) can influence plants during the restoration of rocky desertification are not thoroughly understood. This study used mulberry as a suitable example of a mycorrhizal plant in desertification areas to examine changes in growth, leaf carbon, nitrogen accumulation, and the carbon–nitrogen ratio post inoculation using AMF. The correlation between leaf carbon–nitrogen ratio and root morphology following AMF inoculation was also examined. The results demonstrated that inoculating mulberry with the dominant strains Funneliformis mosseae (Fm) and Rhizophagus intraradices (Ri) not only enhanced above-ground growth and improved carbon and nitrogen nutrient absorption but also had a more pronounced effect on leaf nitrogen accumulation than on carbon accumulation, resulting in a potential decrease in the leaf C:N ratio by 42.13%. It also significantly improved root morphology by exponentially increasing the number of connections and crossings by 120.5% and 109.8%, respectively. Further analysis revealed a negative correlation between leaf C:N ratio and root morphology, as well as between root length and the number of connections. Plants with more developed root systems exhibited greater competitiveness for nitrogen, resulting in a lower leaf C:N ratio. This study suggests that the inoculation of AMF could enhance leaf nitrogen accumulation and reduce the leaf C:N ratio by expanding the spatial absorption range of the root through positive changes in root morphology, thereby promoting plant nutrient growth. This study forms a fundamental scientific basis for the successful management of desertification. Full article

Vegetation net primary productivity (NPP) is critical to maintaining and enhancing the carbon sink of vegetation. Shaoguan is a characteristic forest city in the subtropical region of South China and an ecological barrier in the Guangdong–Hong Kong–Macau Greater Bay Area (GBA), playing an instrumental role in protecting water resources, purifying air, and maintaining ecological balance. However, studies that quantify subtropical vegetation NPP dynamics in Shaoguan under the influence of climate and human drivers are still incomplete. In this research, vegetation NPP at 30 m resolution was estimated from 2001 to 2020 using the enhanced CASA model based on the GF-SG algorithm in Shaoguan. The RESTREND method was then utilized to quantify climatic and human effects on NPP. The results indicated that the vegetation NPP in Shaoguan increased rapidly (4.09 g C/m²/yr, p < 0.001) over the past 20 years. Climate and human drivers contributed 0.948 g C/m²/yr and 3.137 g C/m²/yr to vegetation NPP, respectively. Human activity plays a major role in vegetation restoration through ecological projects, whereas vegetation deterioration is primarily attributable to the combined action of climate change and human activity, such as urban expansion, deforestation, and meteorological disasters. The results emphasize the importance of ecological projects for the restoration of vegetated ecosystems and ecological construction in Shaoguan. Full article

Identifying the changes in landscape pattern and ecosystem service value (ESV) and clarifying their relationship in temporal changes and spatial variations can provide insight into regional landscape features and scientific support for regional landscape planning. Leveraging land use data from the Yihe River Basin, we quantitatively assessed the landscape pattern and ESV shifts spanning from 2000 to 2018 using the landscape pattern indexes and the equivalence factor method. We employed Pearson correlation metrics and the geographically weighted regression model to explore the interrelation of their spatiotemporal variations. Our results show the following: (1) Forestland represents the most expansive land cover category. Apart from construction land, all other types experienced a decline in area. The most notable change occurred in the area of construction land. (2) The aggregation of the overall landscape shows a downward trend. The levels of fragmentation, landscape diversity, and richness increased. (3) Throughout the entire study period, the overall ESV gradually decreased, and the land cover type with the greatest contribution to the ESV was forestland. (4) In terms of temporal changes, the patch density and edge density of the overall area are significantly negatively correlated with total ESVs. The largest values for the patch index, perimeter–area fractal dimension (PAFRAC), and aggregation are significantly positively correlated with total ESVs. (5) In terms of spatial variation, the contagion index (CONTAG), PAFRAC, and the Shannon diversity index (SHDI) were noticeably correlated with ESVs. The CONTAG is positively correlated with ESVs upstream, but negatively midstream and downstream. The SHDI is negatively correlated with ESVs upstream, but positively midstream and downstream. The PAFRAC exhibits a positive correlation with ESVs for the most part. The association between the landscape pattern indexes and ESVs exhibits temporal and spatial inconsistencies in most instances, suggesting a spatiotemporal scale effect in their relationship. This study recommends that the local government devises a long-term strategy for urban development and exercises stringent control over the unregulated expansion of construction land. Through reasonable territorial spatial planning, government departments could enhance the connectivity of the overall landscape pattern of the Yihe River Basin to achieve the reasonable allocation and sustainable development of regional resources. Full article

The increased frequency of climate change-induced droughts poses a survival challenge for forest trees, particularly for the common beech (Fagus sylvatica L.). Drought conditions adversely affect water supply and nutrient uptake, yet there is limited understanding of the intricate interplay between nutrient availability and drought stress on the physiology, growth, and biomass accumulation in young trees. We aimed to address this knowledge gap by examining the effects of irrigation and fertilisation and their interaction with various parameters in common beech saplings, including foliar and root N, P, and K concentrations; height and diameter increments; and aboveground and belowground biomass production. Our findings revealed that a higher fertilisation dose increased nutrient availability, also partially mitigating immediate drought impacts on foliar N concentrations. Also, higher fertilisation supported the post-drought recovery of foliar phosphorus levels in saplings. Prolonged drought affected nitrogen and potassium foliar concentrations, illustrating the lasting physiological impact of drought on beech trees. While drought-stressed beech saplings exhibited reduced height increment and biomass production, increased nutrient availability positively impacted root collar diameters. These insights have potential implications for forest management practices, afforestation strategies, and our broader understanding of the ecological consequences of climate change on forests. Full article

Chinese fir is one of the most widely distributed and extensively planted timber species in China. Therefore, monitoring pests and diseases in Chinese fir plantations is directly related to national timber forest security and forest ecological security. This study aimed to identify appropriate vegetation indices for the early monitoring of pests and diseases in Chinese fir plantations. For this purpose, the researchers used an imaging spectrometer to capture hyperspectral images of both experimental and control groups. The experimental group consisted of Chinese fir trees with two sections of bark stripped off, while the control group consisted of healthy Chinese fir trees. The study then assessed the sensitivity of 11 vegetation indices to the physiological differences between the two groups using the Mann–Whitney U test. The results showed that both the green-to-red region spectral angle index (GRRSGI) and the red edge position index (REP) were able to monitor the difference as early as 16 days after damage. However, GRRSGI performs best in monitoring early death changes in Chinese fir trees because it is less affected by noise and is more stable. The green–red spectral area index (GRSAI) also had high stability, but the monitoring effect was slightly worse than that of GRRSGI and REP. Compared with other indices, GRRSGI and GRSAI can better exploit the advantages of hyperspectral data. Full article

The Belian (Eusideroxylon zwageri Teijsm. & Binn.) is a commercially important timber species in Southeast Asia that was listed on the IUCN Red List of threatened species in 1998. Six years ago, we published an article in Genome Biology Ecology entitled “Evolutionary Comparisons of the Chloroplast Genome in Lauraceae and Insights into Loss Events in the Magnoliids” in which one complete plastid genome of Belian was assembled for comparative analyses of the plastomes in Lauraceae. However, a recent study concluded that our sequenced Belian individual can be located in the clade of Myristicaceae instead of that of Lauraceae. Here, we performed reanalyses of an additional two Belian plastomes, along with 42 plastomes from plants spanning 10 families of the Magnoliids. The three Belian plastomes are 39% CG and vary in length from 157,535 to 157,577 bp. A total of 37 tRNA genes, 8 rRNA genes, and 85 protein-coding genes were among the 130 annotated genes. There were 95–101 repeat sequences and 56–61 simple repeat sequences (SSRs). Comparative genomic analysis revealed 170 mutation sites in their plastomes, which include 111 substitutions, 53 indels, and 6 microinversions. Phylogeny was reconstructed using maximum-likelihood and Bayesian approaches for 44 magnoliids species, indicating that the 3 Belian individuals were nested among the species in the Lauraceae family rather than Myristicaceae. Full article

Land-use change has a great impact on regional ecosystem balance and carbon storage, so it is of great significance to study future land-use types and carbon storage in a region to optimize the regional land-use structure. Based on the existing land-use data and the different scenarios of the shared socioeconomic pathway and the representative concentration pathway (SSP-RCP) provided by CMIP6, this study used the PLUS model to predict future land use and the InVEST model to predict the carbon storage in the study area in the historical period and under different scenarios in the future. The results show the following: (1) The change in land use will lead to a change in carbon storage. From 2000 to 2020, the conversion of cultivated land to construction land was the main transfer type, which was also an important reason for the decrease in regional carbon storage. (2) Under the three scenarios, the SSP126 scenario has the smallest share of arable land area, while this scenario has the largest share of woodland and grassland land area, and none of the three scenarios shows a significant decrease in woodland area. (3) From 2020 to 2050, the carbon stocks in the study area under the three scenarios, SSP126, SSP245, and SSP585, all show different degrees of decline, decreasing to 36,405.0204 × 10⁴ t, 36,251.4402 × 10⁴ t, and 36,190.4066 × 10⁴ t, respectively. Restricting the conversion of land with a high carbon storage capacity to land with a low carbon storage capacity is conducive to the benign development of regional carbon storage. This study can provide a reference for the adjustment and management of future land-use structures in the region. Full article

The increase in global carbon emissions has intensified the effects of CO₂ fertilization on the carbon cycle. CO₂ fertilization is shaped by several factors, including the physiological differences among trees of varied forest ages and types, as well as the influence of different climatic conditions. It is essential to investigate the differences in CO₂ fertilization effects across diverse climate zones and delve into the association between these effects and forest age and type. Such exploration will deepen our knowledge of forest responses to environmental changes. This study used annual ring width data from the International Tree-Ring Data Bank, employing the generalized additive mixed models and the Random Forest model to discern the pattern of the CO₂ fertilization effect concerning forest age in the Northern Hemisphere. This study also explored the variations in the effect of CO₂ fertilization across unique climate zones and the disparities among various forest types within the same climatic zone. The results indicated a link between forest age and the CO₂ fertilization effect: it tends to increase in sapling forests and middle-aged forests and diminish in mature forests. Warmer, drier environments had a more marked effect of increased CO₂ on tree fertilization. Additionally, coniferous forests demonstrated a more substantial CO₂ fertilization effect than broadleaf forests, and deciduous needle-leaf forests surpassed evergreen needle-leaf forests in this regard. This research is pivotal in understanding the shifting patterns of CO₂ fertilization effects and how forests respond to atmospheric changes. Full article

Forest fires result in severe disaster, causing significant ecological damage and substantial economic losses. Flames and smoke represent the predominant characteristics of forest fires. However, these flames and smoke often exhibit irregular shapes, rendering them susceptible to erroneous positive or negative identifications, consequently compromising the overall performance of detection systems. To enhance the average precision and recall rates of detection, this paper introduces an enhanced iteration of the You Only Look Once version 5 (YOLOv5) algorithm. This advanced algorithm aims to achieve more effective fire detection. First, we use Switchable Atrous Convolution (SAC) in the backbone network of the traditional YOLOv5 to enhance the capture of a larger receptive field. Then, we introduce Polarized Self-Attention (PSA) to improve the modeling of long-range dependencies. Finally, we incorporate Soft Non-Maximum Suppression (Soft-NMS) to address issues related to missed detections and repeated detections of flames and smoke by the algorithm. Among the plethora of models explored, our proposed algorithm achieves a 2.0% improvement in mean Average [email protected] (mAP⁵⁰) and a 3.1% enhancement in Recall when compared with the YOLOv5 algorithm. The integration of SAC, PSA, and Soft-NMS significantly enhances the precision and efficiency of the detection algorithm. Moreover, the comprehensive algorithm proposed here can identify and detect key changes in various monitoring scenarios. Full article

The effect of pruning treatments on growth, photosynthesis characteristics, and metabolites were was studied in Eucommia ulmoides Oliver (E. ulmoides). The experiment was carried out from March–August 2019. Three treatments were used: non-pruned trees (CK), a height of 20 cm above the top edge of the flowerpot (T1), and a height of 10 cm above the top edge of the flowerpot (T2). The results showed that the branches branch number, leaves leaf number, and stem diameter increased significantly (p < 0.05) in pruning treatments compared with CK. Similarly, the net photosynthetic rate (Pn), stomatal conductance (Gs), transpiration rate (Tr), maximum photosynthetic efficiency (Fv/Fm), and non-photochemical quenching coefficient (NPQ) increased significantly in pruning treatments (p < 0.05). Interestingly, the contents of Chl a, Chl b, Chl, Car, and the rate between the Chl a content and the Chl b content increased significantly (p < 0.05) in T2, respectively. These verified that it was a better way to enhance the plants growth of E. ulmoides for pruning treatments. The GC-MS analysis showed that 36 different primary metabolites were identified, including 11 sugars, 13 acids, 5 alcohols, and 7 other compounds, the relative content of their metabolites were was higher in the T2 treatment than that in the T1 treatment, which was mainly concentrated in four main enrichment pathways (Galactose metabolism; Citrate cycle; Glyoxylate and dicarboxylate metabolism; and starch and sucrose metabolism) via KEGG analysis. Meanwhile, correlation analysis showed there were was a positive correlation between the accumulation of D-Galactose, D-Mannose, Succinic acid, and photosynthetic pigment content, and the rate of photosynthesis in T2 treatment (p < 0.05). The pruning height above the top edge of the flowerpot changed the accumulation of primary metabolites and promoted plant regeneration ability in E. ulmoides. Finally, the yield of main secondary metabolites from leaves (Genipin, Geniposide, Geniposidic acid, and Pinoresinol diglucoside) were was increased in pruning treatments by UPLC analysis. It provided a reference for the directional ecological cultivation of E. ulmoides. Full article

In recent years, frequent global climate change has led to extreme weather events, such as high temperatures and droughts. Under the backdrop of climate change, the potential distribution zones of plants will undergo alterations. Therefore, it is necessary to predict the potential geographical distribution patterns of plants under climate change. Schima superba, a plant species with significant ecological and economic value, plays a crucial role in ecological restoration and maintaining environmental stability. Therefore, predicting potential changes in its suitable habitat in Zhejiang Province is significant. The MaxEnt model and combined data from 831 monitoring sites where Schima superba is distributed in Zhejiang Province with 12 selected bioclimatic variables were used to predict habitat suitability adaptability. We found that (1) the average AUC value of the MaxEnt model in repeated experiments was 0.804, with a standard deviation of 0.014, which indicates high reliability in predictions. (2) The total suitable habitat area for Schima superba in Zhejiang Province (suitability value > 0.05) is 87,600 km², with high-suitability, moderate-suitability, and low-suitability areas covering 29,400 km², 25,700 km², and 32,500 km², respectively. (3) Likewise, elevation, precipitation, and temperature are the dominant climatic variables that influence the distribution of Schima superba. Schima superba mainly occurs in areas with an elevation above 500 m and precipitation over 140 mm during the hottest season. The probability of Schima superba distribution reaches its peak at elevations between 1200 and 1400 m. Here, the precipitation ranges from 300 to 350 mm with high humidity, between 160 and 170 mm during the hottest season, and an annual temperature range between 28 and 31 °C. Therefore, our results indicate that climate change significantly affects the suitable habitat area of Schima superba. We also reveal the ecological characteristics and adaptation mechanisms of Schima superba in different geographical regions of Zhejiang Province. Future research should focus on the relationship between plant adaptation strategies and environmental changes, as well as applications in ecosystem protection and sustainable development, to promote the development and application of plant habitat adaptability research. Full article

Deforestation remains one of the key concerning activities around the world due to commodity-driven extraction, agricultural land expansion, and urbanization. The effective and efficient monitoring of national forests using remote sensing technology is important for the early detection and mitigation of deforestation activities. Deep learning techniques have been vastly researched and applied to various remote sensing tasks, whereby fully convolutional neural networks have been commonly studied with various input band combinations for satellite imagery applications, but very little research has focused on deep networks with high-resolution representations, such as HRNet. In this study, an optimal semantic segmentation architecture based on high-resolution feature maps and an attention mechanism is proposed to label each pixel of the satellite imagery input for forest identification. The selected study areas are located in Malaysian rainforests, sampled from 2016, 2018, and 2020, downloaded using Google Earth Pro. Only a two-class problem is considered for this study, which is to classify each pixel either as forest or non-forest. HRNet is chosen as the baseline architecture, in which the hyperparameters are optimized before being embedded with an attention mechanism to help the model to focus on more critical features that are related to the forest. Several variants of the proposed methods are validated on 6120 sliced images, whereby the best performance reaches 85.58% for the mean intersection over union and 92.24% for accuracy. The benchmarking analysis also reveals that the attention-embedded high-resolution architecture outperforms U-Net, SegNet, and FC-DenseNet for both performance metrics. A qualitative analysis between the baseline and attention-based models also shows that fewer false classifications and cleaner prediction outputs can be observed in identifying the forest areas. Full article

The Siberian silkmoth is one of the most dangerous coniferous forests pests. Siberian silkmoth outbreaks cause massive defoliation and subsequent forest fires over vast areas. Remote forest disturbance assessments performed after an outbreak make it possible to assess carbon emissions and the potential for natural regeneration, estimate forest fire danger, and reveal the need to implement forest management practices. The goal of the present research was to investigate the use of modern satellite imagery of medium spatial resolution to estimate the percentage of dead trees in a given area. The subject of this study is the Siberian silkmoth outbreak that occurred in 2018–2020 and covered 42 thousand ha in the Irbey region of the Krasnoyarsk Krai. Imagery from the Sentinel-2/MSI sensor was used to calculate a number of spectral indices for images received before and after the outbreak. Field study data were used to create regression models relating the index values to the percentage of dead trees. A number of spectral indices, such as NDVI, dNDVI, NBR, dNBR, NDMI, EVI, and TCG, were used. As a result, spectral indices based on the data from NIR/SWIR bands (NBR, NDMI, dNBR) demonstrated the best correlations with field-measured tree mortality. Therefore, these indices may be used to accurately estimate the percentage of dead trees by remote sensing data. The best was the NBR index with an R² equal to 0.87, and the lowest RMSE and MAE errors. Consequently, Sentinel-2 imagery can be successfully used for tree mortality assessment over large inaccessible areas disturbed by Siberian silkmoth outbreaks at a relatively low cost. Full article

Bamboo shoots are a healthy vegetable with significant commercial value, and their appearance quality is a key factor influencing consumer preference and market pricing. Their growth characteristics—after being unearthed, they maintain basal diameter while rapidly growing in height—affect the taste and nutritional quality. However, little attention has been given to the impact of bamboo forest management on shoot appearance. Therefore, this study addressed this research gap through a comprehensive investigation across three types of bamboo forests: evergreen broad-leaved forest (EBF), evergreen deciduous broad-leaved mixed forest (MBF), and pure bamboo forest (PBF). In addition, we further assessed factors that potentially affect the appearance quality of bamboo shoots, including canopy structures, understory light factors and understory soil factors, mother bamboo factors, and shoot internal factors (pigments and cells). The basal diameters of shoots in PBF and MBF were 1.89 cm and 1.97 cm, respectively, which were significantly larger than those in EBF by 0.27 cm and 0.35 cm, respectively. The linear mixed effect model identified the number of bamboo rhizome internodes and the chlorophyll a content as primary factors influencing basal diameter thickening and elongation growth of shoots, respectively. In addition, increasing the bamboo canopy and mean leaf angle reduced the chlorophyll a content and increased the carotenoid content, thereby benefiting the improvement in or maintenance of the taste and quality of shoots. This study highlighted that increasing the number of bamboo rhizome internodes, bamboo canopy, and mean leaf angle is helpful to improve the appearance quality of shoots. These findings offer a scientific foundation for bamboo forest management, contributing to both ecological sustainability and economic benefits. Full article